Users spend an immense amount of time interacting with content on social media websites. On one popular social media website, for example, over a billion active users spend a total of over ten million hours each month interacting with the website. These users can often produce hundreds of millions of content posts each day. In response to user access, the social media website can select content such as other users' posts, news feeds, event notifications, and advertisements to display to the users. Selecting content items that users are likely to find helpful or relevant increases the chances that users will interact with those content items and that they will return to the website in the future.
Over time, topics discussed on social media fall into and out of favor. Topics that are discussed above a threshold amount, either as a numerical total or relative to other topics, are referred to as “trending.” Determining trending topics can be extremely valuable in selecting content items or in convincing advertisers to utilize social media channels to reach potential customers. For example, trending topics can be helpful to inform marketing decisions, to provide recommendations for other users, to predict resource usage, to draw analogies to other similar topics and actions, etc. However, classifying a topic as trending can be difficult. For example, trends that may exist for a segment of social media contributors, such as those who share a particular geographical location, may not be readily apparent from an analysis of general social media posts. Furthermore, performing an in-depth analysis on combinations of the billions of social media posts that are created every month can become computationally intractable. Furthermore, determining topics that are currently trending is often not as useful as predicting topics that will be trending in the future. However, identifying such trending topics as predictions for the future adds another layer of technical complexity that further limits the ability of systems in the prior art to provide useful topic identifications.
The techniques introduced here may be better understood by referring to the following Detailed Description in conjunction with the accompanying drawings, in which like reference numerals indicate identical or functionally similar elements.